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Record W2101295059 · doi:10.1002/atr.1248

A new kind of fundamental diagram with an application to road traffic emission modeling

2013· article· en· W2101295059 on OpenAlexvenueno aff
Vincent Aguiléra, Antoine Tordeux

Bibliographic record

VenueJournal of Advanced Transportation · 2013
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
FundersU.S. Environmental Protection Agency
KeywordsOccupancyMode (computer interface)DiagramTraffic flow (computer networking)Distribution (mathematics)Empirical distribution functionFlow (mathematics)Function (biology)Distribution functionStatistical physicsComputer scienceMechanicsMathematicsEngineeringPhysicsStatisticsMathematical analysisThermodynamics

Abstract

fetched live from OpenAlex

SUMMARY The main contribution of this paper is to show, on the basis of empirical traffic data, that the distribution of vehicle speeds on a road segment evolves with the occupancy in a simple manner. Under a critical occupancy, the distribution is unimodal, with one peak (the high mode) close to the free‐flow speed. When the occupancy exceeds the critical occupancy, the distribution of speeds becomes bimodal. A second peak (the low mode) appears, at a noticeably lower speed than the high mode. Empirical speed distributions are well fitted when assuming a (appropriately scaled and translated) low “temperature” Maxwell–Boltzmann distribution for the high mode and a high‐temperature distribution for the low mode. The standard fundamental diagram expresses the mean flow speed as a function of occupancy. The model proposed in this paper expresses the distribution of vehicle speeds as a function of the occupancy. We believe this result to be of great importance for both the theory of traffic flow and practical applications. Copyright © 2013 John Wiley & Sons, Ltd.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.209
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2013
Admission routes1
Has abstractyes

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